arXiv subjects

Peng Jiang

Publications and source records attributed to Peng Jiang.

At least 91 records · Page 5Linked to original sources

arXiv2026

Unveiling a Thin Filament of the Cosmic Web in the Ursa Major Supergroup

Filaments are crucial components of the cosmic web, representing the extensive and aligned distributions of galaxies and gas. Using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we report the detection of a filament in the Ursa Major supergroup using atomic-hydrogen (HI) observations. This filament consists of sixteen various types of galaxies and five starless gas clumps, spanning a length of approximately 0.9 Mpc. Notably, it is extremely thin, with a thickness comparable to the diameter of a galaxy. We observed a galaxy-filament spin alignment and a velocity gradient within the filament. These findings strongly suggest a cold accretion flow along the filament, potentially contributing to the formation and growth of the galaxies. The thin filament, as a small group, is likely to be merged into the Ursa Major supergroup in the context of hierarchical structure formation.

astro-ph.CO↗

arXiv2026

Think-Then-Generate: Reasoning-Aware Text-to-Image Diffusion with LLM Encoders

Recent progress in text-to-image (T2I) diffusion models (DMs) has enabled high-quality visual synthesis from diverse textual prompts. Yet, most existing T2I DMs, even those equipped with large language model (LLM)-based text encoders, remain text-pixel mappers -- they employ LLMs merely as text encoders, without leveraging their inherent reasoning capabilities to infer what should be visually depicted given the textual prompt. To move beyond such literal generation, we propose the think-then-generate (T2G) paradigm, where the LLM-based text encoder is encouraged to reason about and rewrite raw user prompts; the states of the rewritten prompts then serve as diffusion conditioning. To achieve this, we first activate the think-then-rewrite pattern of the LLM encoder with a lightweight supervised fine-tuning process. Subsequently, the LLM encoder and diffusion backbone are co-optimized to ensure faithful reasoning about the context and accurate rendering of the semantics via Dual-GRPO. In particular, the text encoder is reinforced using image-grounded rewards to infer and recall world knowledge, while the diffusion backbone is pushed to produce semantically consistent and visually coherent images. Experiments show substantial improvements in factual consistency, semantic alignment, and visual realism across reasoning-based image generation and editing benchmarks, achieving 0.79 on WISE score, nearly on par with GPT-4. Our results constitute a promising step toward next-generation unified models with reasoning, expression, and demonstration capacities.

cs.CV↗

arXiv2026

First Submillimeter Lights from Dome A: Tracing the Carbon Cycle in the Feedback of Massive Stars

The cycling of carbon between its ionized, atomic, and molecular phases shapes the chemical compositions and physical conditions of the interstellar medium (ISM). However, ground-based studies of the full carbon cycle have been limited by atmospheric absorption. Dome~A, the most promising site for submillimeter astronomy, has long resisted successful submillimeter astronomical observations. Using the 60~cm Antarctic Terahertz Explorer, we present the first successful CO ($4-3$) and [CI] ($^3P_1 - ^3P_0$) mapping observations of two archetypal triggered massive star-formation regions at Dome~A. These data, together with archival [CII], provide the first complete characterization of all three carbon phases in these environments. We find elevated C$^{0}$/CO abundance ratios in high-extinction regions, plausibly driven by deep penetration of intense radiation fields from massive stars into a clumpy ISM. These findings mark a major milestone for submillimeter astronomy at Dome~A and offer valuable insights into the impact of massive star feedback on the surrounding ISM.

astro-ph.GA↗

arXiv2025

Antarctic TianMu Staring Observation Project I: Overview and Implementation of the Prototype Telescope

Wide-field rapid sky surveys serve as critical observational methods for time-domain astronomical research. The Antarctic region, with several months of continuous dark nights annually, is an ideal site for time-domain astronomical observations. The Antarctic TianMu Staring Observation Project aims to deploy a fleet of small telescopes, adopting an array observation model to conduct time-domain optical observations in Antarctica, featuring wide-sky coverage, high-cadence sampling, long-period staring, and simultaneous multi-band measurements. Considering the severe challenges optical telescopes face in Antarctica, including extremely low temperatures, unattended operation, and limited power supply and network transmission, we have designed and developed the Antarctic TianMu prototype telescope based on drift-scan charge-coupled device technology. In October 2022, our prototype (with an aperture of 18 cm), named AT-Proto was transported to Zhongshan Station in Antarctica aboard China's 39th Antarctic Research Expedition. It has since operated stably and reliably in the frigid environment for over two years, demonstrating the significant advantages of this technology in polar astronomical observations. The experimental observation results of AT-Proto provide a solid foundation for the subsequent construction of a time-domain astronomy observation array in Antarctica.

astro-ph.IM↗

arXiv2025

Experimental study on the wall-pressure fluctuations of flow over an axisymmetric hull

Wall pressure fluctuations beneath the turbulent boundary layer of high-speed underwater vehicles are crucial for hydro-acoustics and acoustic stealth. However, a comprehensive understanding remains limited due to a lack of high-quality experimental data, particularly under realistic operational conditions. To address this gap, this study establishes the first high-fidelity experimental database of wall-pressure fluctuations on an axisymmetric hull at high Reynolds numbers. The dataset's primary innovation is its systematic inclusion of complex maneuvering (yaw and pitch) conditions, providing a benchmark for validating flow noise prediction models. Analysis of this dataset yields key physical insights. The study quantifies systematic Reynolds number effects, including a spectral energy shift toward lower frequencies, and spectral scaling laws by revealing the critical influence of pressure-gradient effects. These findings provide fundamental insights into non-equilibrium 3D turbulent flows and establish an essential dataset to support the design of quieter and more effective underwater vehicles.

physics.flu-dyn↗

arXiv2025

Gas Accretion from a Neighbouring Galaxy Fuels the Low-luminosity AGN in NGC 4278

How a seemingly `dead' host galaxy provides fuel for its active galactic nuclei (AGN) remains an unresolved problem. Using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we present a new high-sensitivity atomic-hydrogen (HI) observation toward the nearby elliptical galaxy NGC 4278 and its adjacent region. From the observation, we found that external gas accretion from a neighbouring galaxy fuels the low-luminosity AGN in NGC 4278 through tidal interactions. The accreted gas entering NGC 4278 exhibits a rotating gas disk. And the accreted galaxy has been gas-poor and has an HI to stellar mass ratio of about 0.02. Due to the process of gas accretion, it is likely that relativistic jets are generated in the AGN of NGC 4278. The emission of TeV gamma rays in NGC 4278 is likely to be associated with the newly accreted HI gas.

astro-ph.GA↗

arXiv2025

Reputation-Based Leader Election under Partial Synchrony: Towards a Protocol-Independent Abstraction with Enhanced Guarantees

Leader election serves a well-defined role in leader-based Byzantine Fault Tolerant (BFT) protocols. Existing reputation-based leader election frameworks for partially synchronous BFTs suffer from either protocol-specific proofs, narrow applicability, or unbounded recovery after network stabilization, leaving an open problem. This paper presents a novel protocol-independent abstraction formalizing generic correctness properties and effectiveness guarantees for leader election under partial synchrony, enabling protocol-independent analysis and design. Building on this, we design the Sliding Window Leader Election (SWLE) mechanism. SWLE dynamically adjusts leader nominations via consensus-behavior-based reputation scores, enforcing Byzantine-cost amplification. We demonstrate SWLE introduces minimal extra overhead to the base protocol and prove it satisfies all abstraction properties and provides superior effectiveness. We show, with a 16-server deployment across 4 different regions in northern China, SWLE achieves up to 4.2x higher throughput, 75% lower latency and 27% Byzantine leader frequency compared to the state-of-the-art solution under common Byzantine faults, while maintaining efficiency in fault-free scenarios.

cs.DC↗

arXiv2025

External-field-induced transition from altermagnetic metal to fully-compensated ferrimagnetic metal in monolayer $\mathrm{Cr_2O}$

Altermagnets and fully-compensated ferrimagnets are two canonical classes of zero-net-moment magnets. An altermagnetic (AM) half-metal cannot exist due to its AM spin splitting, while a fully-compensated ferrimagnetic (FC-FIM) metal seems impossible to realize because both spin channels remain gapless. Here, we propose that an FC-FIM metal can be realized by breaking the rotational or mirror symmetry that links two spin-opposite magnetic atoms in an AM metal. We further demonstrate that charge-carrier doping is fundamentally unable to generate a net magnetic moment in an altermagnet, whereas such a net moment can be readily induced in a fully-compensated ferrimagnet. We use the AM monolayer $\mathrm{Cr_2O}$ as a concrete example to validate our proposal. Either electric field or uniaxial strain can break the $S_{4z}$ symmetry of $\mathrm{Cr_2O}$, thereby inducing a transition from an AM metal to an FC-FIM metal. Uniaxial strain plus carrier doping creates a net moment in an altermagnet, and the so-called piezomagnetism is essentially a strain-driven switch from altermagnetism to fully-compensated ferrimagnetism. By analogy, we advance the concept of electromagnetism: an electric field drives the transition from altermagnetism to fully-compensated ferrimagnetism, and subsequent charge-carrier doping stabilizes a net magnetization. Our work provides a roadmap for further exploring the connection and distinction between altermagnet and fully-compensated ferrimagnet, and confirms the feasibility of FC-FIM metal.

cond-mat.mtrl-sci↗

arXiv2025

EEA: Exploration-Exploitation Agent for Long Video Understanding

Long-form video understanding requires efficient navigation of extensive visual data to pinpoint sparse yet critical information. Current approaches to longform video understanding either suffer from severe computational overhead due to dense preprocessing, or fail to effectively balance exploration and exploitation, resulting in incomplete information coverage and inefficiency. In this work, we introduce EEA, a novel video agent framework that archives exploration-exploitation balance through semantic guidance with hierarchical tree search process. EEA autonomously discovers and dynamically updates task-relevant semantic queries, and collects video frames closely matched to these queries as semantic anchors. During the tree search process, instead of uniform expansion, EEA preferentially explores semantically relevant frames while ensuring sufficient coverage within unknown segments. Moreover, EEA adaptively combines intrinsic rewards from visionlanguage models (VLMs) with semantic priors by explicitly modeling uncertainty to achieve stable and precise evaluation of video segments. Experiments across various long-video benchmarks validate the superior performance and computational efficiency of our proposed method.

cs.CV↗

arXiv2025

DAS: Dual-Aligned Semantic IDs Empowered Industrial Recommender System

Semantic IDs are discrete identifiers generated by quantizing the Multi-modal Large Language Models (MLLMs) embeddings, enabling efficient multi-modal content integration in recommendation systems. However, their lack of collaborative signals results in a misalignment with downstream discriminative and generative recommendation objectives. Recent studies have introduced various alignment mechanisms to address this problem, but their two-stage framework design still leads to two main limitations: (1) inevitable information loss during alignment, and (2) inflexibility in applying adaptive alignment strategies, consequently constraining the mutual information maximization during the alignment process. To address these limitations, we propose a novel and flexible one-stage Dual-Aligned Semantic IDs (DAS) method that simultaneously optimizes quantization and alignment, preserving semantic integrity and alignment quality while avoiding the information loss typically associated with two-stage methods. Meanwhile, DAS achieves more efficient alignment between the semantic IDs and collaborative signals, with the following two innovative and effective approaches: (1) Multi-view Constrative Alignment: To maximize mutual information between semantic IDs and collaborative signals, we first incorporate an ID-based CF debias module, and then design three effective contrastive alignment methods: dual user-to-item (u2i), dual item-to-item/user-to-user (i2i/u2u), and dual co-occurrence item-to-item/user-to-user (i2i/u2u). (2) Dual Learning: By aligning the dual quantizations of users and ads, the constructed semantic IDs for users and ads achieve stronger alignment. Finally, we conduct extensive offline experiments and online A/B tests to evaluate DAS's effectiveness, which is now successfully deployed across various advertising scenarios at Kuaishou App, serving over 400 million users daily.

cs.IR↗

arXiv2025

Align$^3$GR: Unified Multi-Level Alignment for LLM-based Generative Recommendation

Large Language Models (LLMs) demonstrate significant advantages in leveraging structured world knowledge and multi-step reasoning capabilities. However, fundamental challenges arise when transforming LLMs into real-world recommender systems due to semantic and behavioral misalignment. To bridge this gap, we propose Align$^3$GR, a novel framework that unifies token-level, behavior modeling-level, and preference-level alignment. Our approach introduces: Dual tokenization fusing user-item semantic and collaborative signals. Enhanced behavior modeling with bidirectional semantic alignment. Progressive DPO strategy combining self-play (SP-DPO) and real-world feedback (RF-DPO) for dynamic preference adaptation. Experiments show Align$^3$GR outperforms the SOTA baseline by +17.8% in Recall@10 and +20.2% in NDCG@10 on the public dataset, with significant gains in online A/B tests and full-scale deployment on an industrial large-scale recommendation platform.

cs.IR↗

arXiv2025

A possible periodic RM evolution in the repeating FRB 20220529

Fast radio bursts (FRBs) are mysterious millisecond-duration radio transients of extragalactic origin. Some of them repeat, while others apparently do not. Investigations of periodic activity in repeating FRB have been conducted to probe their origins. While periodicity in the burst rate has been reported, studies of periodicities in other properties, such as dispersion measure (DM) and rotation measure (RM), are sparse. FRB~20220529 was monitored by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) for nearly three years, providing an opportunity to investigate periodicity in its observed properties. Here we report a possible period of $\sim 200$ days in the RM evolution, with a significance of {4.1 $σ$} estimated via the Lomb-Scargle algorithm and {3.1 $σ$} with the phase-folding method. Periodicity in the burst rate was also investigated. It may indicate that the FRB progenitor is in a binary system, which is consistent with the significant RM increase and prompt recovery of this FRB on a week-timescale. Other scenarios, such as a system with an intermediate-mass black hole, are also explored.

astro-ph.HE↗

arXiv2025

Multiwavelength Observations of the Apparently Non-repeating FRB 20250316A

The physical origin of fast radio bursts (FRBs) remains uncertain. Although multiwavelength observations have been widely conducted, only Galactic FRB~20200428D is associated with an X-ray burst from the magnetar SGR J1935+2154. Here, we present multiwavelength follow-up observations of the nearby bright FRB~20250316A, including the Five-hundred-meter Aperture Spherical radio Telescope (FAST), Einstein Probe (EP) X-ray mission, Chandra X-ray Observatory, Wide Field Survey Telescope (WFST) and Space Variable Object Monitor/Visible Telescope (SVOM/VT). The 13.08-hour FAST follow-up campaign without pulse detection requires an energy distribution flatter than those of well-known repeating FRBs, suggesting that this burst is likely a one-off event. A prompt EP follow-up and multi-epoch observational campaign totaling $>$ 100 ks led to the detection of an X-ray source within the angular resolution of its Follow-up X-ray Telescope (FXT, $10^{\prime\prime}$). A subsequent Chandra observation revealed this source to be offset by $7^{\prime\prime}$ from the FRB position, and established a 0.5-10 keV flux upper limit of $7.6\times 10^{-15}$ $\rm erg\,cm^{-2}\,s^{-1}$ at the FRB position, corresponding to $\sim 10^{39}$ $\rm erg\,s^{-1}$ at the 40 Mpc distance of the host galaxy NGC~4141. These results set one of the most stringent limits on X-ray emission from a non-repeating FRB, disfavoring ultra-luminous X-ray sources (ULXs) as counterparts of apparently one-off FRBs and offering critical insights into afterglow models. Our study suggests that an arcsecond localization of both the FRB and its potential X-ray counterpart is essential for exploring the X-ray counterpart of an FRB.

astro-ph.HE↗

arXiv2025

MindRec: A Diffusion-driven Coarse-to-Fine Paradigm for Generative Recommendation

Recent advancements in large language model-based recommendation systems often represent items as text or semantic IDs and generate recommendations in an auto-regressive manner. However, due to the left-to-right greedy decoding strategy and the unidirectional logical flow, such methods often fail to produce globally optimal recommendations. In contrast, human reasoning does not follow a rigid left-to-right sequence. Instead, it often begins with keywords or intuitive insights, which are then refined and expanded. Inspired by this fact, we propose MindRec, a diffusion-driven coarse-to-fine generative paradigm that emulates human thought processes. Built upon a diffusion language model, MindRec departs from auto-regressive generation by leveraging a masked diffusion process to reconstruct items in a flexible, non-sequential manner. Particularly, our method first generates key tokens that reflect user preferences, and then expands them into the complete item, enabling adaptive and human-like generation. To further emulate the structured nature of human decision-making, we organize items into a hierarchical category tree. This structure guides the model to first produce the coarse-grained category and then progressively refine its selection through finer-grained subcategories before generating the specific item. To mitigate the local optimum problem inherent in greedy decoding, we design a novel beam search algorithm, Diffusion Beam Search, tailored for our mind-inspired generation paradigm. Experimental results demonstrate that MindRec yields a 9.5\% average improvement in top-1 accuracy over state-of-the-art methods, highlighting its potential to enhance recommendation performance. The implementation is available via https://github.com/Mr-Peach0301/MindRec.

cs.IR↗

arXiv2025

Graph Learning for Cooperative Cell-Free ISAC Systems: From Optimization to Estimation

Cell-free integrated sensing and communication (ISAC) systems have emerged as a promising paradigm for sixth-generation (6G) networks, enabling simultaneous high-rate data transmission and high-precision radar sensing through cooperative distributed access points (APs). Fully exploiting these capabilities requires a unified design that bridges system-level optimization with multi-target parameter estimation. This paper proposes an end-to-end graph learning approach to close this gap, modeling the entire cell-free ISAC network as a heterogeneous graph to jointly design the AP mode selection, user association, precoding, and echo signal processing for multi-target position and velocity estimation. In particular, we propose two novel heterogeneous graph learning frameworks: a dynamic graph learning framework and a lightweight mirror-based graph attention network (mirror-GAT) framework. The dynamic graph learning framework employs structural and temporal attention mechanisms integrated with a three-dimensional convolutional neural network (3D-CNN), enabling superior performance and robustness in cell-free ISAC environments. Conversely, the mirror-GAT framework significantly reduces computational complexity and signaling overhead through a bi-level iterative structure with share adjacency. Simulation results validate that both proposed graph-learning-based frameworks achieve significant improvements in multi-target position and velocity estimation accuracy compared to conventional heuristic and optimization-based designs. Particularly, the mirror-GAT framework demonstrates substantial reductions in computational time and signaling overhead, underscoring its suitability for practical deployments.

eess.SP↗

arXiv2025

Generative Sequential Recommendation via Hierarchical Behavior Modeling

Recommender systems in multi-behavior domains, such as advertising and e-commerce, aim to guide users toward high-value but inherently sparse conversions. Leveraging auxiliary behaviors (e.g., clicks, likes, shares) is therefore essential. Recent progress on generative recommendations has brought new possibilities for multi-behavior sequential recommendation. However, existing generative approaches face two significant challenges: 1) Inadequate Sequence Modeling: capture the complex, cross-level dependencies within user behavior sequences, and 2) Lack of Suitable Datasets: publicly available multi-behavior recommendation datasets are almost exclusively derived from e-commerce platforms, limiting the validation of feasibility in other domains, while also lacking sufficient side information for semantic ID generation. To address these issues, we propose a novel generative framework, GAMER (Generative Augmentation and Multi-lEvel behavior modeling for Recommendation), built upon a decoder-only backbone. GAMER introduces a cross-level interaction layer to capture hierarchical dependencies among behaviors and a sequential augmentation strategy that enhances robustness in training. To further advance this direction, we collect and release ShortVideoAD, a large-scale multi-behavior dataset from a mainstream short-video platform, which differs fundamentally from existing e-commerce datasets and provides pretrained semantic IDs for research on generative methods. Extensive experiments show that GAMER consistently outperforms both discriminative and generative baselines across multiple metrics.

cs.IR↗

arXiv2025

Differentiable Fast Top-K Selection for Large-Scale Recommendation

Cascade ranking is a widely adopted paradigm in large-scale information retrieval systems for Top-K item selection. However, the Top-K operator is non-differentiable, hindering end-to-end training. Existing methods include Learning-to-Rank approaches (e.g., LambdaLoss), which optimize ranking metrics like NDCG and suffer from objective misalignment, and differentiable sorting-based methods (e.g., ARF, LCRON), which relax permutation matrices for direct Top-K optimization but introduce gradient conflicts through matrix aggregation. A promising alternative is to directly construct a differentiable approximation of the Top-K selection operator, bypassing the use of soft permutation matrices. However, even state-of-the-art differentiable Top-K operator (e.g., LapSum) require $O(n \log n)$ complexity due to their dependence on sorting for solving the threshold. Thus, we propose DFTopK, a novel differentiable Top-K operator achieving optimal $O(n)$ time complexity. By relaxing normalization constraints, DFTopK admits a closed-form solution and avoids sorting. DFTopK also avoids the gradient conflicts inherent in differentiable sorting-based methods. We evaluate DFTopK on both the public benchmark RecFLow and an industrial system. Experimental results show that DFTopK significantly improves training efficiency while achieving superior performance, which enables us to scale up training samples more efficiently. In the online A/B test, DFTopK yielded a +1.77% revenue lift with the same computational budget compared to the baseline. To the best of our knowledge, this work is the first to introduce differentiable Top-K operators into recommendation systems and the first to achieve theoretically optimal linear-time complexity for Top-K selection. We have open-sourced our implementation to facilitate future research in both academia and industry.

cs.LG↗

arXiv2025

Accelerating Mixture-of-Experts Inference by Hiding Offloading Latency with Speculative Decoding

Recent advancements in Mixture of Experts (MoE) models have significantly increased their parameter scale as well as model performance. Extensive offloading techniques have been proposed to address the GPU memory limitations of MoE inference. However, due to the I/O bottleneck and sparse computation of MoE models, existing offloading techniques still suffer from low hardware utilization. To fully utilize the hardware resources, we propose SpecMoEOff, which employs the speculative decoding technique to enlarge the workload of each expert. SpecMoEOff orchestrates the GPU and CPU by both theoretical and empirical roofline analysis. In addition, we develop a dedicated CPU chunked attention verification kernel to fit the speculative decoding in offloading scenarios as well as minimizing the additional overhead led by draft models. SpecMoEOff further integrates an optimizer to automatically tune the hyperparameters of speculative decoding under given hardware and workload. Experimental results show that SpecMoEOff achieves up to 2.5x decode throughput improvement over the state-of-the-art MoE offloading techniques.

cs.DC↗